Automated read-across workflow for predicting acute oral toxicity: I. The decision scheme in the QSAR toolbox.

A decision-scheme outlining the steps for identifying the appropriate chemical category and subsequently appropriate tested source analog(s) for data gap filling of a target chemical by read-across is described. The primary features used in the grouping of the target chemical with source analogues within a database of 10,039 discrete organic substances include reactivity mechanisms associated with protein interactions and specific-acute-oral-toxicity-related mechanisms (e.g., mitochondrial uncoupling). Additionally, the grouping of chemicals making use of the in vivo rat metabolic simulator and neutral hydrolysis. Subsequently, a series of structure-based profilers are used to narrow the group to the most similar analogues. The scheme is implemented in the OECD QSAR Toolbox, so it automatically predicts acute oral toxicity as the rat oral LD50 value in log [1/mol/kg]. It was demonstrated that due to the inherent variability in experimental data, classification distribution should be employed as more adequate in comparison to the exact classification. It was proved that the predictions falling in the adjacent GSH categories to the experimentally-stated ones are acceptable given the variation in experimental data. The model performance estimated by adjacent accuracy was found to be 0.89 and 0.54 while based on R2. The mechanistic and predictive coverages were >0.85.

The implementation of RAAF in the OECD QSAR Toolbox.

The Read-Across Assessment Framework (RAAF) was developed by the European Chemicals Agency (ECHA) as an internal tool providing a framework for a consistent, structured and transparent assessment of grouping of chemicals and read-across. Following a RAAF-based evaluation, also developers and users of read-across predictions outside ECHA can judge whether their read-across rationale is sufficiently robust from a regulatory perspective. The aim of this paper is to describe the implementation of RAAF functionalities in the OECD QSAR Toolbox report. These can be activated in the prediction report after performing a readacross prediction. Once the user manually selects the appropriate scenario, the RAAF assessment elements appear and are automatically aligned with the suitable category elements of the Toolbox report. Subsequently, these are evaluated as part of the category consistency assessment functionality. The implementation of the RAAF functionality is illustrated in practice with two examples.

Mutagenicity in Surface Waters: Synergistic Effects of Carboline Alkaloids and Aromatic Amines.

For decades, mutagenicity has been observed in many surface waters with a possible link to the presence of aromatic amines. River Rhine is a well-known example of this phenomenon but responsible compound(s) are still unknown. To identify the mutagenic compounds, we applied effect-directed analysis (EDA) utilizing novel analytical and biological approaches to a water sample extract from the lower Rhine. We could identify 21 environmental contaminants including two weakly mutagenic aromatic amines, and the known alkaloid comutagen norharman along with two related ß-carboline alkaloids, carboline, and 5-carboline, which were reported the first time in surface waters. Results of mixture tests showed a strong synergism of the identified aromatic amines not only with norharman, but also with carboline and 5-carboline. Additionally, other nitrogen-containing compounds also contributed to the mutagenicity when aromatic amines were present. Thus, comutagenicity of ß-carboline alkaloids with aromatic amines is shown to occur in surface waters. These results strongly suggest that surface water mutagenicity is highly complex and driven by synergistic mechanisms of a complex compound mixture (of which many are yet unidentified) rather than by single compounds. Therefore, mixture effects should be considered not only from mutagens alone, but also including possible comutagens and nonmutagenic compounds.

Computational material flow analysis for thousands of chemicals of emerging concern in European waters.

Knowledge of exposure to a wide range of chemicals, and the spatio-temporal variability thereof, is urgently needed in the context of protecting and restoring aquatic ecosystems. This paper discusses a computational material flow analysis to predict the occurrence of thousands of man-made organic chemicals on a European scale, based on a novel temporally and spatially resolved modelling framework. The goal was to increase understanding of pressures by emerging chemicals and to complement surface water monitoring data. The ambition was to provide a first step towards a "real-life" mixture exposure situation accounting for as many chemicals as possible. Comparison of simulated concentrations and chemical monitoring data for 226 substance/basin combinations showed that the simulated concentrations were accurate on average. For 65% and 90% of substance/basin combinations the error was within one and two orders of magnitude respectively. An analysis of the relative importance of uncertainties revealed that inaccuracies in use volume or use type information contributed most to the error for individual substances. To resolve this, we suggest better registration of use types of industrial chemicals, investigation of presence/absence of industrial chemicals in wastewater and runoff samples and more scientific information exchange.

UVCB substances II: Development of an endpoint-nonspecific procedure for selection of computationally generated representative constituents.

Substances of unknown or variable composition, complex reaction products, and biological materials (UVCBs) comprise approximately 40% of all registered substances submitted to the European Chemicals Agency. One of the main characteristics of UVCBs is that they have no unique representation. Industry scientists who are part of the scientific community have been working with academics and consultants to address the problem of a lack of a defined structural description. It has been acknowledged that one of the obstacles is the large number of possible structural isomers. We have recently proposed and published a methodology, based on the generic substance identifiers, to address this issue. The methodology allows for the coding of constituents, their generation, calculation of important characteristics of UVCB constituents, and selection of representative constituents. In the present study we introduce a statistical selection of the minimum number of generated constituents representing a UVCB. This representative sample was selected in such a way that the structural variability and the properties of concern of the UVCB were approximated within a predefined tolerable error. The aim of the statistical selection was to enable the assessment of UVCB substances by decreasing the number of constituents that need to be evaluated. The procedure, which was shown to be endpoint-independent, was validated theoretically and on real case studies. Environ Toxicol Chem 2019;38:682-694. © 2019 SETAC.